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International Conference on Intelligent Transportation

作者： Yao Long Teng Htet Naing Wentong Cai College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Lane change intention prediction is critical in improving road safety. In general, this task is achieved via two distinct approaches: The data-based approach and the rule-based approach. Generally, the former outperforms the latter under in-distribution scenarios. However, under out-distribution scenarios, data-based methods tend to perform extremely poorly. Rule-based methods are robust to such problems since they are formulated based on theory. Instead of relying exclusively on each approach, it is more advantageous to combine them to exploit both of their strengths. Thus, in this paper a physics guided hybrid model is proposed for lane change intention prediction to improve model robustness under distribution shift. The effectiveness of the proposed model is demonstrated in the experimental results where the proposed model outperforms rule-based models under in-distribution scenarios by 4%, and outperforms both rule-based models and data-based models in out-distribution scenarios by 3% and 60% respectively in the highD dataset.

关键词： Training Transforms Predictive models Prediction algorithms Robustness Road safety data models Calibration Physics Intelligent transportation systems

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A Framework for Identifying the Learners' Engagement in E-learning Environments Using a Hybrid CNN Architecture 6

A Framework for Identifying the Learners' Engagement in E-le...

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6th International Conference on computing, Electronics and Communications Engineering, iCCECE 2023

作者： Anny, Tasfia Nuzhat Chowdhury, Jafrin Iqbal Ahsan, Tanveer Zahur, Istiaque Patwary, Muhammed J. A. Miraz, Mahdi H. International Islamic University Computer Science and Engineering Chittagong Bangladesh Sonargaon University Mechanical Engineering Bangladesh Xiamen University Malaysia School of Computing and Data Science Malaysia

ISBN: (纸本)9798350340921

In the realm of online education, precise identification of the participants and their engagement is critical for optimal learning results. With the aim of precisely monitoring attendance and engagement in an online learning environment, this study suggests an end-To-end framework based on a hybrid convolutional neural network (CNN) architecture. The complete evaluation employs two separate CNN models: CNN-I for identifying learners' faces in individual images and CNN-II for measuring engagement through facial expressions, eye gaze and head movement. Two custom datasets have been developed and annotated to allow for an in-depth assessment of the proposed approach. dataset-I contains 4000 individual face images, whereas dataset-II has 6720 images depicting various interactions of the learners, such as engaged, bored, frustrated, confused, drowsy and looking away. However, on dataset-I and the VGG-face dataset, the CNN-I model surpasses a deep CNN (DCNN) with superior accuracy rates of 95.25% and 88.60%, respectively. On dataset-II and the FER2013 dataset, the CNN-II model obtains similarly outstanding accuracy rates of 93.45% and 83.74%, respectively. The specialised CNN-I and CNN-II models outperform the hybrid CNN model in their particular tasks, albeit having an accuracy rate of 88%. The research highlights the enormous potential of straightforward architecture integrated with specific datasets, emphasising their computational effectiveness and efficiency in the domains of engagement and facial recognition for online learning. © 2023 IEEE.

关键词： Face recognition

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Research on the Detection Methods for Changes in the Mangroves of Dongzhaigang, Hainan from 2019 to 2023 24

Research on the Detection Methods for Changes in the Mangrov...

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8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Xianyao Lu, Huajun Qiu, Shuailong Liu, Lijin Wu, Zhongqiang School of Information Science and Technology Hainan Normal University Hainan Haikou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400710094

This study introduces the research methods for detecting changes in the mangroves of Dongzhaigang, Hainan from 2019 to 2023. Mangrove ecosystems play a crucial role in providing habitats and have ecological and economic value, but in recent years, they have faced degradation and management issues. In order to effectively monitor mangrove changes, researchers have utilized remote sensing techniques and machine learning methods for mangrove cover information extraction and classification. The paper describes the methods of estimating mangrove cover using remote sensing satellite imagery and spectral indices and explores the applications of clustering, decision trees, support vector machines, and deep learning in mangrove change detection. The research results indicate that the support vector machine algorithm performs well in classifying mangrove and non-mangrove areas. This study provides a basis for the management and decision-making of Dongzhaigang Mangrove Nature Reserve in Hainan Province and highlights the future research potential in the application of multi-source and multi-resolution data and deep learning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Retinal Image Analysis for Early Deduction of Diabetic Retinopathy 9

Retinal Image Analysis for Early Deduction of Diabetic Retin...

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9th International Conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Chinnasamy, A. Selvakumari, P. SRMIST School of Computing Department of Data Science and Business Systems Chennai India Anna University Chennai Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397376

The prevalence of DR is steadily rising, which necessitates the automatic disease severity extraction and classification. About 2% of those with this illness are entirely blind because of the diabetic mellitus problem and 10% get vision impairment after 15 years of diabetes as a result of the DR complication. It is also a significant contributor to blindness in both middle aged and older age groups. The patient may develop to severe stages of irreversible blindness if the condition is not detected early. The growing number of diabetic patients face a major issue due to a lack of ophthalmologists. It is suggested that an automated DR screening system be created to aid the ophthalmologist in making decisions. One of the primary symptoms of the DR is hard exudates. The detection of hard exudates is crucial for screening purposes and aids in disease monitoring and diagnosis. Thus, utilising Lloyd's clustering technique, this work offered a unique techniques to segment the exudates and irregularities in DR were found. © 2023 IEEE.

关键词： Mean square error

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Analysis and Performance Evaluation of Credit Card Fraud by Multi-model ML 3

Analysis and Performance Evaluation of Credit Card Fraud by ...

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3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Islam, Md Aminul Asif Imran, A.T.M. Rahman, Md Habibur Pabel, Md Amran Hossen Mishra, Bhupesh Kumar Basu, Kashinath University of Gloucestershire Data Science School of Engineering and Technology Cheltenham United Kingdom Dhaka Bangladesh University of Chittagong Department of Mathematics Chittagong Bangladesh Wright State University Master of Science in Business Analytics United States United Kingdom Oxford Brookes University Data Science School of Engineering Computing and Mathematics United Kingdom

ISBN: (纸本)9798350388282

Using credit cards is a convenient and efficient payment mechanism. Credit card fraud significantly impacts financial loss, mental health, and the reputation of financial institutions. This study would incorporate an analysis of the preceding statement about eliminating several obstacles associated with the availability of public data, the presence of unbalanced data, the dynamic nature of fraud tendencies, and the prevalence of false alarms. The authors discuss various machine-learning techniques used to detect credit card fraud. Extreme Learning, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, and XG Boost are among these techniques on the European card benchmark dataset. A comparative evaluation of the effectiveness of machine learning has also been conducted, and precision is increased by incorporating multiple layers. The study's findings indicate significant improvements in several crucial metrics, including accuracy, f1-score, precision, and AUC curves. XGBoost model achieves optimized values of 99.9\% accuracy. The proposed model outperforms contemporary machine learning approaches in credit card fraud detection. It has been observed that the combination of data balancing techniques significantly reduces the occurrence of false negatives. This study has substantial potential for credit card fraud detection applications in the real world, providing an effective and efficient method for addressing this ongoing critical issue. © 2024 IEEE.

关键词： Decision trees

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Fast Bipartitioned Hybrid Adder Utilizing Carry Select and Carry Lookahead Logic

Fast Bipartitioned Hybrid Adder Utilizing Carry Select and C...

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International Conference on Mathematics and Computers in sciences and in Industry (MCSI)

作者： Padmanabhan Balasubramanian Douglas L. Maskell College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350377224

ISBN: (纸本)9798350377231

We present a novel fast bipartitioned hybrid adder (FBHA) that utilizes carry-select and carry-lookahead logic. The proposed FBHA is an accurate adder with a significant part and a less significant part joined together by a carry signal. In an N-bit FBHA, the K-bit less significant part is realized using carry-lookahead adder logic, and the (N – K)-bit significant part is realized using carry-select adder logic. The 32-bit addition was considered as an example operation for this work. Many 32-bit adders ranging from the slow ripple carry adder to the fast parallel-prefix Kogge-Stone adder and the proposed adder were synthesized using a 28-nm CMOS standard cell library and their design metrics were compared. A well-optimized FBHA achieved significant optimizations in design metrics compared to its high-speed adder counterparts and some examples are mentioned as follows: (a) 19.8% reduction in delay compared to a carry-lookahead adder; (b) 19.8% reduction in delay, 24.4% reduction in area, and 19.4% reduction in power compared to a carry-select adder; (c) 45.6% reduction in delay, and 13.5% reduction in power compared to a conditional sum adder; and (d) 46.5% reduction in area, and 29.3% reduction in power compared to the Kogge-Stone adder

关键词： Accuracy Microprocessors Computer architecture Libraries Hybrid power systems Delays Logic Adders Standards Arithmetic

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series data

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IEEE Transactions on Services computing 2025年第03期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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Efficient Beamforming Feedback Information-Based Wi-Fi Sensing by Feature Selection

arXiv

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arXiv 2024年

作者： Li, Xin Hu, Jingzhi Luo, Jun The College of Computing and Data Science Nanyang Technological University Singapore

Wi-Fi sensing leveraging plain-text beamforming feedback information (BFI) in multiple-input-multiple-output (MIMO) systems attracts increasing attention. However, due to the implicit relationship between BFI and the channel state information (CSI), quantifying the sensing capability of BFI poses a challenge in building efficient BFI-based sensing algorithms. In this letter, we first derive a mathematical model of BFI, characterizing its relationship with CSI explicitly, and then develop a closed-form expression of BFI for 2×2 MIMO systems. To enhance the efficiency of BFI-based sensing by selecting only the most informative features, we quantify the sensing capacity of BFI using the Cramer-Rao bound (CRB) and then propose an efficient CRB-based BFI feature selection algorithm. Simulation results verify that BFI and CSI exhibit comparable sensing capabilities and that the proposed algorithm halves the number of features, reducing 20% more parameters than baseline methods, at the cost of only slightly increasing positioning errors. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

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Injective coloring of edge corona product of graphs 8

Injective coloring of edge corona product of graphs

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2024 8th International Conference on Mechanics, Mathematics and Applied Physics, ICMMAP 2024

作者： Zhao, Wenying Tian, Shuangliang School of Mathematics and Computer Science Northwest for Minzu University Lanzhou730030 China Key Laboratory of Streaming Data Computing Technologies and Applications Northwest for Minzu University Lanzhou730030 China

The injective chromatic number χi(G) of a graph G is the minimum number of colors needed to color the vertices of G such that different vertices with the same neighbor are colored differently. In this here, we get some exact values of the injective chromatic number of the edge corona product of some graphs. © Published under licence by IOP Publishing Ltd.

关键词： Graph theory

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LanEvil: Benchmarking the Robustness of Lane Detection to Environmental Illusions 24

LanEvil: Benchmarking the Robustness of Lane Detection to En...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Tianyuan Wang, Lu Li, Hainan Xiao, Yisong Liang, Siyuan Liu, Aishan Liu, Xianglong Tao, Dacheng SKLCCSE Beihang University China Shen Yuan Honors College Beihang University China Institute of Dataspace China School of Computing National University of Singapore Singapore Singapore College of Computing & Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798400706868

Lane detection (LD) is an essential component of autonomous driving systems, providing fundamental functionalities like adaptive cruise control and automated lane centering. Existing LD benchmarks primarily focus on evaluating common cases, neglecting the robustness of LD models against environmental illusions such as shadows and tire marks on the road. This research gap poses significant safety challenges since these illusions exist naturally in real-world traffic situations. For the first time, this paper studies the potential threats caused by these environmental illusions to LD and establishes the first comprehensive benchmark LanEvil for evaluating the robustness of LD against this natural corruption. We systematically design 14 prevalent yet critical types of environmental illusions (e.g., shadow, reflection) that cover a wide spectrum of real-world influencing factors in LD tasks. Based on real-world environments, we create 94 realistic and customizable 3D cases using the widely used CARLA simulator, resulting in a dataset comprising 90,292 sampled images. Through extensive experiments, we benchmark the robustness of popular LD methods using LanEvil, revealing substantial performance degradation (-5.37% Accuracy and -10.70% F1-Score on average), with shadow effects posing the greatest risk (-7.39% Accuracy). Additionally, we assess the performance of commercial auto-driving systems OpenPilot and Apollo through collaborative simulations, demonstrating that proposed environmental illusions can lead to incorrect decisions and potential traffic accidents. To defend against environmental illusions, we propose the Attention Area Mixing (AAM) approach using hard examples, which witness significant robustness improvement (+3.76%) under illumination effects. We hope our paper can contribute to advancing more robust auto-driving systems in the future. Part of our dataset and demos can be found at the https://***/. © 2024 ACM.

关键词： Adaptive cruise control

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